Storage capacity planning can extend the life of hardware. Storage hardware assets are very expensive relative to their useful life. Underutilized systems compound financial challenges and further drain IT budgets. Conventional wisdom encourages VARs to help customers track data storage utilization with metrics like "percent allocated" in order to baseline, then improve effectiveness with capacity optimization and a proper storage capacity plan.

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But the "utilization rate" approach fails to take into account several key aspects of storage in business. First, it is disastrous to run out of disk capacity on a production application as database engines don't tolerate full file systems. Additionally, storage prices are falling and storage capacities are on the rise. Disk purchased too soon not only ties up capital earlier than necessary, but the customer misses opportunities to acquire more storage at a better price point.

Solution providers should look to our forefathers in business for advice. Free storage capacity should be managed like a business manages inventory. In this tech tip, I'll stop talking about storage effectiveness in terms of "percent allocated" and start talking in terms of days of inventory remaining.

Conventional data storage capacity planning

Most storage shops run at less than 50% utilized, while organizations maintaining 80% utilized or better are often recognized for superior storage capacity planning management skills. Incredible amounts of effort go in to calculating utilization and allocation metrics. But they really don't produce a clear picture of effectiveness because the percent-utilized metrics do not take into account growth rate.

Let's assume that a 10 terabyte (TB) shop sets a goal to be 80% allocated. When the objective is achieved, they will have 8 TB allocated and 2000 gigabytes (GB) free. If the aggregate growth rate of all applications is 10 GB/day it will take 200 days to run out of capacity. Set the same 80% goal for a shop that grows 100 GB/day, and the 80% target only yields 20 days before crisis. Running out of capacity means death, so we need a metric that helps us ensure that your customers will never run out. The capacity metrics should be based on time, not percentages.

Poor storage capacity planning raises storage inventory turnover rate

Business scholars have studied similar challenges for centuries; the discipline is called inventory management. Business planners have developed an inventory capacity planning metric that is rooted in time, called inventory turnover rate. This is a common measure used to determine the cost effectiveness of a business' inventory. The following fictional company leverages the inventory turnover rate metric to improve profits.

The Blue Bomber Bicycle Company spends $100 to make each of its best-selling beach cruiser bicycles. The retail price is $125, and they sell about 1,000 bikes per year. Every December, they order 1,000 bikes for $100,000 and store them in warehouses. At the end of each year, after they have sold all the bikes, the gross sales will be $125K. The balance sheet reports that the company invested $100K to make $25K; not bad. The entire inventory is filled and depleted once per year so the inventory turnover rate is 1.

In an effort to improve profitability, the Blue Bomber Bicycle Company takes the advice of a consultant (smart move!) and orders 250 bicycles four times per year, instead of 1,000 bikes once per year. The cost to stock up for three months is now only $25K instead of $100K. The inventory turnover rate is now 4. At the end of the year, they report that they tied up only $25K of capital to earn $25K; this is great! The other $75K can be invested in additional opportunities, greatly improving profitability.

This simple example illustrates how businesses tune the number of inventory turnovers per year in order to meet the needs of their customers and shareholders. The inventory turnover metric is also often expressed in the number of days of inventory. An inventory turnover value of 1 means, on average, they always have about 180 days of product on hand (365/1/2). A turnover rate of 4 means that on average they have 45 days of inventory on hand (365/4/2).

Determine storage rate of consumption

Applying inventory management principals to storage capacity management and free space is not all that difficult, and it provides important insight into a storage shop's efficiency. Storage managers have to control an immense number of variables when planning storage purchases. Senior managers trained in business will understand the concept of storage capacity as measured in the number of days remaining, as compared to more esoteric terms like gigabytes and terabytes, or meaningless numbers like 88% allocated.

The first step in using the turnover rates in storage is to find a method to determine the rate of consumption. Most storage resource management tools are woefully inadequate in this respect. On the positive side, tracking the gross growth rates is surprisingly simple and can be done in a spreadsheet. Keep a daily, weekly or monthly tally of utilization metrics for each pool of storage and use the Excel trend function to get the growth rate.

Next, determine the frequency your customer needs to make storage purchases to replenish. Take into account aspects like the customer's financial periods and preferred disk suppliers, growth trend volatility, the appropriate safety stock and any technological constraints that may discourage making very small disk purchases.

Finally, report capacity trends and improvements based on the number of days of inventory as opposed to the percent utilized.

Storage growth, procurement costs and falling prices challenge us to think about capacity in more mature terms. Managing into a specific storage inventory turnover period lowers the capital required to operate the storage service, improves the cost basis by which storage managers acquire storage and guarantees an appropriate amount of capacity to serve the business needs.

About the author:Brian Peterson is an independent IT Infrastructure Analyst. He has a deep background in enterprise storage and open systems computing platforms. A recognized expert in his field, he held positions of great responsibility on both the supplier and customer sides of IT.

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